Purpose

To explore R books. How we use them, value them, and discuss them.

Lists

Blogs
So many great books lists for computation and R. Here is a brief list scraped from blog posts captured by R-bloggers.

library(tidyverse)
blogs <- read_csv("data/blogs.csv")
knitr::kable(blogs[1:10, ])
title authors
R for Data Science Hadley Wickham
Advanced R Hadley Wickham
Cookbook for R Winston Chang
The art of R programming Norman Matloff
Hands-On Programming with R Garrett Grolemund
Machine learning with R Brett Lantz
R for Excel Users John Taveras
R Packages Hadley Wickham
ggplot 2: Elegant Graphics for Data Analysis Hadley Wickham
R Cookbook Paul Teetor

Citations
The Web of Science also shockingly now indexes citations to books.

library(tidyverse)
citations <- read_csv("data/citations.csv")
knitr::kable(citations[1:10, ])
title authors year citations citations.per.yr
Numerical Ecology with R Borcard, Daniel; Gillet, Francois; Legendre, Pierre 2011 2535 281.67
Optical Wireless Communications: System and Channel Modelling with MATLAB(R) Ghassemlooy, Z; Popoola, W; Rajbhandari, S 2013 559 79.86
Time Series Analysis and Its Applications: With R Examples, Third Edition Shumway, Robert H.; Stoffer, David S. 2011 349 38.78
Applied Numerical Methods using MATLAB (R) Yang, WY; Cao, W; Chung, TS; Morris, J 2005 231 15.40
Joint Models for Longitudinal and Time-to-Event Data: With Applications in R Rizopoulos, D 2012 186 23.25
Modern Statistical Methods for Astronomy: With R Applications Feigelson, ED; Babu, GJ 2012 181 22.63
Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences Wehrens, Ron 2011 141 15.67
Data Mining with Rattle and R: The Art of Excavationg Data for Knowledge Discovery Williams, Graham 2011 133 14.78
Benchmarking with DEA, SFA, and R Bogetoft, Peter; Otto, Lars 2011 131 14.56
Functional and Phylogenetic Ecology in R Swenson, NG 2014 110 18.33

R Meetup
A brief survey of local R users.

library(tidyverse)
#meetup <- read_csv("data/meetup.csv")
#knitr::kable(meetup[1:10, ])

Data viz

Interpretations

  1. Context matters.
  2. Citations, use, and influence are not always the same.
  3. There is hope for books.